An anti-"shilling attacks" collaborative filtering algorithm based on user trust ranks and items

A collaborative filtering algorithm based on user trust ranks and items is proposed to improve the anti-"shilling attacks" ability.Firstly,a user relationship graph is built based on user interest similarities,rating similarities,and rating correlations.Secondly,using the relationship graph,a userrank model is proposed to calculate user trust ranks.Thirdly,the userrank values are taken as users' weights to incorporated into the typical item-based Slope One algorithm.Finally,we experimentally evaluate our approach and compare it to Slope One.The experiment results suggest that our approach provides better recommendation than Slope One.